Autonomous driving, in recent years, has been receiving increasing attention for its potential
to relieve drivers' burdens and improve the safety of driving. In modern autonomous driving …

In the realm of modern autonomous driving, the perception system is indispensable for
accurately assessing the state of the surrounding environment, thereby enabling informed …

In this paper, we present BEVerse, a unified framework for 3D perception and prediction
based on multi-camera systems. Unlike existing studies focusing on the improvement of …

Autonomous driving is regarded as one of the most promising remedies to shield human
beings from severe crashes. To this end, 3D object detection serves as the core basis of …

In recent years, vision-centric Bird's Eye View (BEV) perception has garnered significant
interest from both industry and academia due to its inherent advantages, such as providing …

Monocular 3D object detection is an important yet challenging task in autonomous driving.
Some existing methods leverage depth information from an off-the-shelf depth estimator to …

Monocular 3D object detection has received increasing attention due to the wide application
in autonomous driving. Existing works mainly focus on introducing geometry projection to …

Monocular 3D object detection has long been a challenging task in autonomous driving.
Most existing methods follow conventional 2D detectors to first localize object centers, and …

Leveraging LiDAR-based detectors or real LiDAR point data to guide monocular 3D
detection has brought significant improvement, eg, Pseudo-LiDAR methods. However, the …

Existing deep learning-based approaches for monocular 3D object detection in autonomous
driving often model the object as a rotated 3D cuboid while the object's geometric shape has …